Overview

Dataset statistics

Number of variables23
Number of observations7000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory184.0 B

Variable types

Categorical10
Numeric13

Alerts

Customer ID has a high cardinality: 7000 distinct values High cardinality
Product ID has a high cardinality: 7000 distinct values High cardinality
Customer ID is uniformly distributed Uniform
Gender is uniformly distributed Uniform
Occupation is uniformly distributed Uniform
Marital Status is uniformly distributed Uniform
Home Ownership is uniformly distributed Uniform
Product ID is uniformly distributed Uniform
Products Purchased is uniformly distributed Uniform
Locations is uniformly distributed Uniform
Customer ID has unique values Unique
Product ID has unique values Unique
Average Purchase Value has unique values Unique
Last Purchase Value has unique values Unique
Total Values of Returns has unique values Unique
Number of Online Purchases has 300 (4.3%) zeros Zeros
Number of Returns has 1132 (16.2%) zeros Zeros

Reproduction

Analysis started2023-03-16 06:39:21.175976
Analysis finished2023-03-16 06:39:44.529101
Duration23.35 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

Customer ID
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct7000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size54.8 KiB
C001
 
1
C4664
 
1
C4675
 
1
C4674
 
1
C4673
 
1
Other values (6995)
6995 

Length

Max length5
Median length5
Mean length4.857285714
Min length4

Characters and Unicode

Total characters34001
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7000 ?
Unique (%)100.0%

Sample

1st rowC001
2nd rowC002
3rd rowC003
4th rowC004
5th rowC005

Common Values

ValueCountFrequency (%)
C0011
 
< 0.1%
C46641
 
< 0.1%
C46751
 
< 0.1%
C46741
 
< 0.1%
C46731
 
< 0.1%
C46721
 
< 0.1%
C46711
 
< 0.1%
C46701
 
< 0.1%
C46691
 
< 0.1%
C46681
 
< 0.1%
Other values (6990)6990
99.9%

Length

2023-03-16T12:09:44.583723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
c0011
 
< 0.1%
c0141
 
< 0.1%
c0041
 
< 0.1%
c0051
 
< 0.1%
c0061
 
< 0.1%
c0071
 
< 0.1%
c0081
 
< 0.1%
c0091
 
< 0.1%
c0101
 
< 0.1%
c0111
 
< 0.1%
Other values (6990)6990
99.9%

Most occurring characters

ValueCountFrequency (%)
C7000
20.6%
13100
9.1%
43100
9.1%
63100
9.1%
53100
9.1%
33100
9.1%
23100
9.1%
72101
 
6.2%
02100
 
6.2%
92100
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27001
79.4%
Uppercase Letter7000
 
20.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
13100
11.5%
43100
11.5%
63100
11.5%
53100
11.5%
33100
11.5%
23100
11.5%
72101
7.8%
02100
7.8%
92100
7.8%
82100
7.8%
Uppercase Letter
ValueCountFrequency (%)
C7000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common27001
79.4%
Latin7000
 
20.6%

Most frequent character per script

Common
ValueCountFrequency (%)
13100
11.5%
43100
11.5%
63100
11.5%
53100
11.5%
33100
11.5%
23100
11.5%
72101
7.8%
02100
7.8%
92100
7.8%
82100
7.8%
Latin
ValueCountFrequency (%)
C7000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII34001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C7000
20.6%
13100
9.1%
43100
9.1%
63100
9.1%
53100
9.1%
33100
9.1%
23100
9.1%
72101
 
6.2%
02100
 
6.2%
92100
 
6.2%

Gender
Categorical

UNIFORM

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size54.8 KiB
Female
3500 
Male
3500 

Length

Max length6
Median length5
Mean length5
Min length4

Characters and Unicode

Total characters35000
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowMale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female3500
50.0%
Male3500
50.0%

Length

2023-03-16T12:09:44.667841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-16T12:09:44.773169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
female3500
50.0%
male3500
50.0%

Most occurring characters

ValueCountFrequency (%)
e10500
30.0%
a7000
20.0%
l7000
20.0%
F3500
 
10.0%
m3500
 
10.0%
M3500
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter28000
80.0%
Uppercase Letter7000
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e10500
37.5%
a7000
25.0%
l7000
25.0%
m3500
 
12.5%
Uppercase Letter
ValueCountFrequency (%)
F3500
50.0%
M3500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin35000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e10500
30.0%
a7000
20.0%
l7000
20.0%
F3500
 
10.0%
m3500
 
10.0%
M3500
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII35000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e10500
30.0%
a7000
20.0%
l7000
20.0%
F3500
 
10.0%
m3500
 
10.0%
M3500
 
10.0%

Age
Real number (ℝ≥0)

Distinct47
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.92542857
Minimum18
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2023-03-16T12:09:44.984146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q129
median41
Q353
95-th percentile62
Maximum64
Range46
Interquartile range (IQR)24

Descriptive statistics

Standard deviation13.49908621
Coefficient of variation (CV)0.3298459339
Kurtosis-1.200306389
Mean40.92542857
Median Absolute Deviation (MAD)12
Skewness-0.001561549572
Sum286478
Variance182.2253284
MonotonicityNot monotonic
2023-03-16T12:09:45.100024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
19170
 
2.4%
29167
 
2.4%
55167
 
2.4%
37165
 
2.4%
56165
 
2.4%
46164
 
2.3%
33164
 
2.3%
23162
 
2.3%
28162
 
2.3%
48161
 
2.3%
Other values (37)5353
76.5%
ValueCountFrequency (%)
18150
2.1%
19170
2.4%
20150
2.1%
21123
1.8%
22125
1.8%
23162
2.3%
24151
2.2%
25151
2.2%
26145
2.1%
27140
2.0%
ValueCountFrequency (%)
64123
1.8%
63149
2.1%
62136
1.9%
61152
2.2%
60150
2.1%
59151
2.2%
58138
2.0%
57153
2.2%
56165
2.4%
55167
2.4%

Income
Real number (ℝ≥0)

Distinct6448
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39839.40357
Minimum20001
Maximum59999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2023-03-16T12:09:45.217007image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum20001
5-th percentile21864.95
Q129726.25
median39764
Q349868.25
95-th percentile57927.4
Maximum59999
Range39998
Interquartile range (IQR)20142

Descriptive statistics

Standard deviation11605.76241
Coefficient of variation (CV)0.2913136586
Kurtosis-1.208830693
Mean39839.40357
Median Absolute Deviation (MAD)10076.5
Skewness0.01322623083
Sum278875825
Variance134693721.1
MonotonicityNot monotonic
2023-03-16T12:09:45.322104image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
252374
 
0.1%
515393
 
< 0.1%
432613
 
< 0.1%
252113
 
< 0.1%
289293
 
< 0.1%
304393
 
< 0.1%
227233
 
< 0.1%
281063
 
< 0.1%
489513
 
< 0.1%
364113
 
< 0.1%
Other values (6438)6969
99.6%
ValueCountFrequency (%)
200011
< 0.1%
200071
< 0.1%
200131
< 0.1%
200141
< 0.1%
200191
< 0.1%
200211
< 0.1%
200222
< 0.1%
200241
< 0.1%
200251
< 0.1%
200351
< 0.1%
ValueCountFrequency (%)
599991
< 0.1%
599731
< 0.1%
599721
< 0.1%
599711
< 0.1%
599701
< 0.1%
599691
< 0.1%
599591
< 0.1%
599451
< 0.1%
599401
< 0.1%
599322
< 0.1%

Occupation
Categorical

UNIFORM

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size54.8 KiB
Student
1000 
Homemaker
1000 
Technician
1000 
Self-employed
1000 
Manager
1000 
Other values (2)
2000 

Length

Max length13
Median length12
Mean length9.571428571
Min length7

Characters and Unicode

Total characters67000
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStudent
2nd rowStudent
3rd rowHomemaker
4th rowStudent
5th rowHomemaker

Common Values

ValueCountFrequency (%)
Student1000
14.3%
Homemaker1000
14.3%
Technician1000
14.3%
Self-employed1000
14.3%
Manager1000
14.3%
Professional1000
14.3%
Executive1000
14.3%

Length

2023-03-16T12:09:45.437816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-16T12:09:45.538063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
student1000
14.3%
homemaker1000
14.3%
technician1000
14.3%
self-employed1000
14.3%
manager1000
14.3%
professional1000
14.3%
executive1000
14.3%

Most occurring characters

ValueCountFrequency (%)
e11000
16.4%
n5000
 
7.5%
a5000
 
7.5%
o4000
 
6.0%
i4000
 
6.0%
l3000
 
4.5%
m3000
 
4.5%
r3000
 
4.5%
c3000
 
4.5%
t3000
 
4.5%
Other values (18)23000
34.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter59000
88.1%
Uppercase Letter7000
 
10.4%
Dash Punctuation1000
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e11000
18.6%
n5000
 
8.5%
a5000
 
8.5%
o4000
 
6.8%
i4000
 
6.8%
l3000
 
5.1%
m3000
 
5.1%
r3000
 
5.1%
c3000
 
5.1%
t3000
 
5.1%
Other values (11)15000
25.4%
Uppercase Letter
ValueCountFrequency (%)
S2000
28.6%
P1000
14.3%
E1000
14.3%
M1000
14.3%
T1000
14.3%
H1000
14.3%
Dash Punctuation
ValueCountFrequency (%)
-1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin66000
98.5%
Common1000
 
1.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e11000
16.7%
n5000
 
7.6%
a5000
 
7.6%
o4000
 
6.1%
i4000
 
6.1%
l3000
 
4.5%
m3000
 
4.5%
r3000
 
4.5%
c3000
 
4.5%
t3000
 
4.5%
Other values (17)22000
33.3%
Common
ValueCountFrequency (%)
-1000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII67000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e11000
16.4%
n5000
 
7.5%
a5000
 
7.5%
o4000
 
6.0%
i4000
 
6.0%
l3000
 
4.5%
m3000
 
4.5%
r3000
 
4.5%
c3000
 
4.5%
t3000
 
4.5%
Other values (18)23000
34.3%

Marital Status
Categorical

UNIFORM

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size54.8 KiB
Single
1750 
Married
1750 
Divorced
1750 
Widowed
1750 

Length

Max length8
Median length7.5
Mean length7
Min length6

Characters and Unicode

Total characters49000
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowSingle
3rd rowSingle
4th rowMarried
5th rowSingle

Common Values

ValueCountFrequency (%)
Single1750
25.0%
Married1750
25.0%
Divorced1750
25.0%
Widowed1750
25.0%

Length

2023-03-16T12:09:45.638301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-16T12:09:45.732826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
single1750
25.0%
married1750
25.0%
divorced1750
25.0%
widowed1750
25.0%

Most occurring characters

ValueCountFrequency (%)
i7000
14.3%
e7000
14.3%
d7000
14.3%
r5250
10.7%
o3500
 
7.1%
S1750
 
3.6%
n1750
 
3.6%
g1750
 
3.6%
l1750
 
3.6%
M1750
 
3.6%
Other values (6)10500
21.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter42000
85.7%
Uppercase Letter7000
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i7000
16.7%
e7000
16.7%
d7000
16.7%
r5250
12.5%
o3500
8.3%
n1750
 
4.2%
g1750
 
4.2%
l1750
 
4.2%
a1750
 
4.2%
v1750
 
4.2%
Other values (2)3500
8.3%
Uppercase Letter
ValueCountFrequency (%)
S1750
25.0%
M1750
25.0%
D1750
25.0%
W1750
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin49000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i7000
14.3%
e7000
14.3%
d7000
14.3%
r5250
10.7%
o3500
 
7.1%
S1750
 
3.6%
n1750
 
3.6%
g1750
 
3.6%
l1750
 
3.6%
M1750
 
3.6%
Other values (6)10500
21.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII49000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i7000
14.3%
e7000
14.3%
d7000
14.3%
r5250
10.7%
o3500
 
7.1%
S1750
 
3.6%
n1750
 
3.6%
g1750
 
3.6%
l1750
 
3.6%
M1750
 
3.6%
Other values (6)10500
21.4%
Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size54.8 KiB
4
1797 
2
1771 
3
1724 
5
1708 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row2
4th row3
5th row4

Common Values

ValueCountFrequency (%)
41797
25.7%
21771
25.3%
31724
24.6%
51708
24.4%

Length

2023-03-16T12:09:45.810935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-16T12:09:45.915406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
41797
25.7%
21771
25.3%
31724
24.6%
51708
24.4%

Most occurring characters

ValueCountFrequency (%)
41797
25.7%
21771
25.3%
31724
24.6%
51708
24.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
41797
25.7%
21771
25.3%
31724
24.6%
51708
24.4%

Most occurring scripts

ValueCountFrequency (%)
Common7000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
41797
25.7%
21771
25.3%
31724
24.6%
51708
24.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII7000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
41797
25.7%
21771
25.3%
31724
24.6%
51708
24.4%

Home Ownership
Categorical

UNIFORM

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size54.8 KiB
Own
3500 
Rent
3500 

Length

Max length4
Median length3.5
Mean length3.5
Min length3

Characters and Unicode

Total characters24500
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOwn
2nd rowOwn
3rd rowOwn
4th rowOwn
5th rowOwn

Common Values

ValueCountFrequency (%)
Own3500
50.0%
Rent3500
50.0%

Length

2023-03-16T12:09:46.002572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-16T12:09:46.101308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
own3500
50.0%
rent3500
50.0%

Most occurring characters

ValueCountFrequency (%)
n7000
28.6%
O3500
14.3%
w3500
14.3%
R3500
14.3%
e3500
14.3%
t3500
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter17500
71.4%
Uppercase Letter7000
 
28.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n7000
40.0%
w3500
20.0%
e3500
20.0%
t3500
20.0%
Uppercase Letter
ValueCountFrequency (%)
O3500
50.0%
R3500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin24500
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n7000
28.6%
O3500
14.3%
w3500
14.3%
R3500
14.3%
e3500
14.3%
t3500
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII24500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n7000
28.6%
O3500
14.3%
w3500
14.3%
R3500
14.3%
e3500
14.3%
t3500
14.3%

Home Value
Real number (ℝ≥0)

Distinct6970
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean527513.5511
Minimum50145
Maximum999983
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2023-03-16T12:09:46.201545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum50145
5-th percentile99381.7
Q1290479.25
median525272
Q3765863.5
95-th percentile956070.25
Maximum999983
Range949838
Interquartile range (IQR)475384.25

Descriptive statistics

Standard deviation274771.5524
Coefficient of variation (CV)0.520880557
Kurtosis-1.208711237
Mean527513.5511
Median Absolute Deviation (MAD)238257
Skewness0.009020335072
Sum3692594858
Variance7.549940599 × 1010
MonotonicityNot monotonic
2023-03-16T12:09:46.323459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7586222
 
< 0.1%
9275142
 
< 0.1%
1062212
 
< 0.1%
2461562
 
< 0.1%
5345502
 
< 0.1%
6438032
 
< 0.1%
1352602
 
< 0.1%
2093352
 
< 0.1%
1687272
 
< 0.1%
4224502
 
< 0.1%
Other values (6960)6980
99.7%
ValueCountFrequency (%)
501451
< 0.1%
503201
< 0.1%
503911
< 0.1%
505201
< 0.1%
505841
< 0.1%
512711
< 0.1%
514151
< 0.1%
516911
< 0.1%
517171
< 0.1%
519981
< 0.1%
ValueCountFrequency (%)
9999831
< 0.1%
9999721
< 0.1%
9998071
< 0.1%
9996171
< 0.1%
9994961
< 0.1%
9993091
< 0.1%
9992381
< 0.1%
9992161
< 0.1%
9990401
< 0.1%
9989991
< 0.1%

Years in Current Home
Real number (ℝ≥0)

Distinct20
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.48928571
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2023-03-16T12:09:46.423697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median10
Q316
95-th percentile19
Maximum20
Range19
Interquartile range (IQR)11

Descriptive statistics

Standard deviation5.795180024
Coefficient of variation (CV)0.5524856679
Kurtosis-1.219619429
Mean10.48928571
Median Absolute Deviation (MAD)5
Skewness0.004721408235
Sum73425
Variance33.58411151
MonotonicityNot monotonic
2023-03-16T12:09:46.511652image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
5374
 
5.3%
1370
 
5.3%
19370
 
5.3%
14365
 
5.2%
4365
 
5.2%
17362
 
5.2%
11361
 
5.2%
6360
 
5.1%
8356
 
5.1%
15353
 
5.0%
Other values (10)3364
48.1%
ValueCountFrequency (%)
1370
5.3%
2348
5.0%
3315
4.5%
4365
5.2%
5374
5.3%
6360
5.1%
7351
5.0%
8356
5.1%
9322
4.6%
10351
5.0%
ValueCountFrequency (%)
20348
5.0%
19370
5.3%
18339
4.8%
17362
5.2%
16347
5.0%
15353
5.0%
14365
5.2%
13321
4.6%
12322
4.6%
11361
5.2%

Credit Score
Real number (ℝ≥0)

Distinct500
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean747.1845714
Minimum500
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2023-03-16T12:09:46.623895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile523
Q1621
median750
Q3869
95-th percentile973
Maximum999
Range499
Interquartile range (IQR)248

Descriptive statistics

Standard deviation144.0245994
Coefficient of variation (CV)0.1927563883
Kurtosis-1.192480138
Mean747.1845714
Median Absolute Deviation (MAD)123
Skewness-0.007655097289
Sum5230292
Variance20743.08523
MonotonicityNot monotonic
2023-03-16T12:09:46.730981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85928
 
0.4%
78427
 
0.4%
55226
 
0.4%
65724
 
0.3%
55824
 
0.3%
83323
 
0.3%
70323
 
0.3%
87523
 
0.3%
51623
 
0.3%
60522
 
0.3%
Other values (490)6757
96.5%
ValueCountFrequency (%)
50016
0.2%
50114
0.2%
50217
0.2%
5037
 
0.1%
50412
0.2%
50521
0.3%
50620
0.3%
50710
0.1%
50814
0.2%
50917
0.2%
ValueCountFrequency (%)
99914
0.2%
99812
0.2%
99712
0.2%
99612
0.2%
99515
0.2%
99413
0.2%
99315
0.2%
99216
0.2%
9917
0.1%
99012
0.2%
Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size54.8 KiB
3
1435 
5
1423 
2
1399 
1
1376 
4
1367 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row1
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
31435
20.5%
51423
20.3%
21399
20.0%
11376
19.7%
41367
19.5%

Length

2023-03-16T12:09:46.824094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-16T12:09:47.024231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
31435
20.5%
51423
20.3%
21399
20.0%
11376
19.7%
41367
19.5%

Most occurring characters

ValueCountFrequency (%)
31435
20.5%
51423
20.3%
21399
20.0%
11376
19.7%
41367
19.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
31435
20.5%
51423
20.3%
21399
20.0%
11376
19.7%
41367
19.5%

Most occurring scripts

ValueCountFrequency (%)
Common7000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
31435
20.5%
51423
20.3%
21399
20.0%
11376
19.7%
41367
19.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII7000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
31435
20.5%
51423
20.3%
21399
20.0%
11376
19.7%
41367
19.5%

Total Credit Card Limit
Real number (ℝ≥0)

Distinct6101
Distinct (%)87.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17474.78814
Minimum5000
Maximum29998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2023-03-16T12:09:47.124468image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5000
5-th percentile6251.8
Q111179.75
median17535
Q323637.75
95-th percentile28631.05
Maximum29998
Range24998
Interquartile range (IQR)12458

Descriptive statistics

Standard deviation7157.089119
Coefficient of variation (CV)0.4095665745
Kurtosis-1.19467901
Mean17474.78814
Median Absolute Deviation (MAD)6192.5
Skewness-0.01130591597
Sum122323517
Variance51223924.66
MonotonicityNot monotonic
2023-03-16T12:09:47.230095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
123955
 
0.1%
122754
 
0.1%
144454
 
0.1%
68854
 
0.1%
187704
 
0.1%
199884
 
0.1%
233773
 
< 0.1%
104153
 
< 0.1%
107353
 
< 0.1%
248393
 
< 0.1%
Other values (6091)6963
99.5%
ValueCountFrequency (%)
50001
< 0.1%
50111
< 0.1%
50162
< 0.1%
50172
< 0.1%
50231
< 0.1%
50241
< 0.1%
50261
< 0.1%
50351
< 0.1%
50381
< 0.1%
50472
< 0.1%
ValueCountFrequency (%)
299981
< 0.1%
299951
< 0.1%
299911
< 0.1%
299901
< 0.1%
299861
< 0.1%
299841
< 0.1%
299741
< 0.1%
299711
< 0.1%
299671
< 0.1%
299612
< 0.1%

Total Credit Card Balance
Real number (ℝ≥0)

Distinct6062
Distinct (%)86.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13085.94186
Minimum1000
Maximum24995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2023-03-16T12:09:47.340479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile2224
Q17145.25
median13115.5
Q319120
95-th percentile23825.15
Maximum24995
Range23995
Interquartile range (IQR)11974.75

Descriptive statistics

Standard deviation6945.270765
Coefficient of variation (CV)0.5307429026
Kurtosis-1.206868805
Mean13085.94186
Median Absolute Deviation (MAD)5998
Skewness-0.01555268547
Sum91601593
Variance48236786
MonotonicityNot monotonic
2023-03-16T12:09:47.456339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
232754
 
0.1%
28504
 
0.1%
222644
 
0.1%
231114
 
0.1%
113743
 
< 0.1%
149823
 
< 0.1%
18893
 
< 0.1%
12543
 
< 0.1%
89303
 
< 0.1%
149863
 
< 0.1%
Other values (6052)6966
99.5%
ValueCountFrequency (%)
10001
< 0.1%
10021
< 0.1%
10031
< 0.1%
10141
< 0.1%
10201
< 0.1%
10261
< 0.1%
10271
< 0.1%
10341
< 0.1%
10361
< 0.1%
10371
< 0.1%
ValueCountFrequency (%)
249951
< 0.1%
249921
< 0.1%
249911
< 0.1%
249901
< 0.1%
249821
< 0.1%
249772
< 0.1%
249751
< 0.1%
249741
< 0.1%
249701
< 0.1%
249661
< 0.1%

Product ID
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct7000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size54.8 KiB
P001
 
1
P4664
 
1
P4675
 
1
P4674
 
1
P4673
 
1
Other values (6995)
6995 

Length

Max length5
Median length5
Mean length4.857285714
Min length4

Characters and Unicode

Total characters34001
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7000 ?
Unique (%)100.0%

Sample

1st rowP001
2nd rowP002
3rd rowP003
4th rowP004
5th rowP005

Common Values

ValueCountFrequency (%)
P0011
 
< 0.1%
P46641
 
< 0.1%
P46751
 
< 0.1%
P46741
 
< 0.1%
P46731
 
< 0.1%
P46721
 
< 0.1%
P46711
 
< 0.1%
P46701
 
< 0.1%
P46691
 
< 0.1%
P46681
 
< 0.1%
Other values (6990)6990
99.9%

Length

2023-03-16T12:09:47.555980image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
p0011
 
< 0.1%
p0141
 
< 0.1%
p0041
 
< 0.1%
p0051
 
< 0.1%
p0061
 
< 0.1%
p0071
 
< 0.1%
p0081
 
< 0.1%
p0091
 
< 0.1%
p0101
 
< 0.1%
p0111
 
< 0.1%
Other values (6990)6990
99.9%

Most occurring characters

ValueCountFrequency (%)
P7000
20.6%
13100
9.1%
43100
9.1%
63100
9.1%
53100
9.1%
33100
9.1%
23100
9.1%
72101
 
6.2%
02100
 
6.2%
92100
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27001
79.4%
Uppercase Letter7000
 
20.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
13100
11.5%
43100
11.5%
63100
11.5%
53100
11.5%
33100
11.5%
23100
11.5%
72101
7.8%
02100
7.8%
92100
7.8%
82100
7.8%
Uppercase Letter
ValueCountFrequency (%)
P7000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common27001
79.4%
Latin7000
 
20.6%

Most frequent character per script

Common
ValueCountFrequency (%)
13100
11.5%
43100
11.5%
63100
11.5%
53100
11.5%
33100
11.5%
23100
11.5%
72101
7.8%
02100
7.8%
92100
7.8%
82100
7.8%
Latin
ValueCountFrequency (%)
P7000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII34001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P7000
20.6%
13100
9.1%
43100
9.1%
63100
9.1%
53100
9.1%
33100
9.1%
23100
9.1%
72101
 
6.2%
02100
 
6.2%
92100
 
6.2%

Products Purchased
Categorical

UNIFORM

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size54.8 KiB
Clothing
1000 
Jewelry
1000 
Books
1000 
Electronics
1000 
Beauty Products
1000 
Other values (2)
2000 

Length

Max length15
Median length11
Mean length10.42857143
Min length5

Characters and Unicode

Total characters73000
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowClothing
2nd rowJewelry
3rd rowJewelry
4th rowBooks
5th rowJewelry

Common Values

ValueCountFrequency (%)
Clothing1000
14.3%
Jewelry1000
14.3%
Books1000
14.3%
Electronics1000
14.3%
Beauty Products1000
14.3%
Home Appliances1000
14.3%
Outdoor Gear1000
14.3%

Length

2023-03-16T12:09:47.641095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-16T12:09:47.744898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
clothing1000
10.0%
jewelry1000
10.0%
books1000
10.0%
electronics1000
10.0%
beauty1000
10.0%
products1000
10.0%
home1000
10.0%
appliances1000
10.0%
outdoor1000
10.0%
gear1000
10.0%

Most occurring characters

ValueCountFrequency (%)
o8000
 
11.0%
e7000
 
9.6%
t5000
 
6.8%
r5000
 
6.8%
c4000
 
5.5%
l4000
 
5.5%
s4000
 
5.5%
3000
 
4.1%
u3000
 
4.1%
i3000
 
4.1%
Other values (19)27000
37.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter60000
82.2%
Uppercase Letter10000
 
13.7%
Space Separator3000
 
4.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o8000
13.3%
e7000
11.7%
t5000
 
8.3%
r5000
 
8.3%
c4000
 
6.7%
l4000
 
6.7%
s4000
 
6.7%
u3000
 
5.0%
i3000
 
5.0%
n3000
 
5.0%
Other values (9)14000
23.3%
Uppercase Letter
ValueCountFrequency (%)
B2000
20.0%
C1000
10.0%
H1000
10.0%
A1000
10.0%
O1000
10.0%
P1000
10.0%
E1000
10.0%
J1000
10.0%
G1000
10.0%
Space Separator
ValueCountFrequency (%)
3000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin70000
95.9%
Common3000
 
4.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o8000
 
11.4%
e7000
 
10.0%
t5000
 
7.1%
r5000
 
7.1%
c4000
 
5.7%
l4000
 
5.7%
s4000
 
5.7%
u3000
 
4.3%
i3000
 
4.3%
n3000
 
4.3%
Other values (18)24000
34.3%
Common
ValueCountFrequency (%)
3000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII73000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o8000
 
11.0%
e7000
 
9.6%
t5000
 
6.8%
r5000
 
6.8%
c4000
 
5.5%
l4000
 
5.5%
s4000
 
5.5%
3000
 
4.1%
u3000
 
4.1%
i3000
 
4.1%
Other values (19)27000
37.0%

Number of Online Purchases
Real number (ℝ≥0)

ZEROS

Distinct21
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.08414286
Minimum0
Maximum20
Zeros300
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2023-03-16T12:09:47.841138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median10
Q315
95-th percentile20
Maximum20
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.05569521
Coefficient of variation (CV)0.6005166027
Kurtosis-1.208525012
Mean10.08414286
Median Absolute Deviation (MAD)5
Skewness-0.006771876937
Sum70589
Variance36.67144447
MonotonicityNot monotonic
2023-03-16T12:09:47.925750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
6369
 
5.3%
18367
 
5.2%
20352
 
5.0%
1345
 
4.9%
4344
 
4.9%
10343
 
4.9%
13341
 
4.9%
14339
 
4.8%
15337
 
4.8%
2336
 
4.8%
Other values (11)3527
50.4%
ValueCountFrequency (%)
0300
4.3%
1345
4.9%
2336
4.8%
3318
4.5%
4344
4.9%
5308
4.4%
6369
5.3%
7327
4.7%
8316
4.5%
9334
4.8%
ValueCountFrequency (%)
20352
5.0%
19330
4.7%
18367
5.2%
17317
4.5%
16323
4.6%
15337
4.8%
14339
4.8%
13341
4.9%
12334
4.8%
11320
4.6%

Average Purchase Value
Real number (ℝ)

UNIQUE

Distinct7000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.7765816
Minimum-80.26384392
Maximum279.2748172
Zeros0
Zeros (%)0.0%
Negative148
Negative (%)2.1%
Memory size54.8 KiB
2023-03-16T12:09:48.025774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-80.26384392
5-th percentile19.19922883
Q166.46785191
median100.5500473
Q3134.5182448
95-th percentile185.2018514
Maximum279.2748172
Range359.5386611
Interquartile range (IQR)68.05039289

Descriptive statistics

Standard deviation50.27271343
Coefficient of variation (CV)0.4988531325
Kurtosis0.001474209729
Mean100.7765816
Median Absolute Deviation (MAD)34.00771678
Skewness0.03838563093
Sum705436.0714
Variance2527.345715
MonotonicityNot monotonic
2023-03-16T12:09:48.141479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
142.7769531
 
< 0.1%
54.971602771
 
< 0.1%
58.22872511
 
< 0.1%
231.27894661
 
< 0.1%
76.975290121
 
< 0.1%
150.68931551
 
< 0.1%
98.528155271
 
< 0.1%
120.80478681
 
< 0.1%
96.201851031
 
< 0.1%
196.08368261
 
< 0.1%
Other values (6990)6990
99.9%
ValueCountFrequency (%)
-80.263843921
< 0.1%
-79.316993691
< 0.1%
-72.581592041
< 0.1%
-67.031301051
< 0.1%
-63.399328611
< 0.1%
-58.198710611
< 0.1%
-54.354001951
< 0.1%
-51.796150661
< 0.1%
-51.54534811
< 0.1%
-49.551971531
< 0.1%
ValueCountFrequency (%)
279.27481721
< 0.1%
270.19918711
< 0.1%
268.2655771
< 0.1%
267.10047131
< 0.1%
266.37357421
< 0.1%
261.55058721
< 0.1%
255.27726541
< 0.1%
254.53243951
< 0.1%
254.40241581
< 0.1%
253.07998781
< 0.1%

Last Purchase Value
Real number (ℝ)

UNIQUE

Distinct7000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.8628423
Minimum-76.62988491
Maximum275.9845657
Zeros0
Zeros (%)0.0%
Negative159
Negative (%)2.3%
Memory size54.8 KiB
2023-03-16T12:09:48.252230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-76.62988491
5-th percentile17.57295736
Q167.77956408
median101.0654707
Q3133.3891747
95-th percentile183.4089178
Maximum275.9845657
Range352.6144506
Interquartile range (IQR)65.60961065

Descriptive statistics

Standard deviation49.95197975
Coefficient of variation (CV)0.4952466006
Kurtosis-0.008606302785
Mean100.8628423
Median Absolute Deviation (MAD)32.77791095
Skewness0.007461356301
Sum706039.8958
Variance2495.200281
MonotonicityNot monotonic
2023-03-16T12:09:48.341847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.131324711
 
< 0.1%
63.227027681
 
< 0.1%
82.006013231
 
< 0.1%
72.966634151
 
< 0.1%
156.24676321
 
< 0.1%
-26.617134171
 
< 0.1%
117.53472051
 
< 0.1%
133.11393751
 
< 0.1%
136.44767721
 
< 0.1%
96.046566141
 
< 0.1%
Other values (6990)6990
99.9%
ValueCountFrequency (%)
-76.629884911
< 0.1%
-76.532031121
< 0.1%
-72.89206181
< 0.1%
-57.353417921
< 0.1%
-56.532520921
< 0.1%
-55.613133531
< 0.1%
-46.574759851
< 0.1%
-46.268919671
< 0.1%
-44.376242791
< 0.1%
-43.496624331
< 0.1%
ValueCountFrequency (%)
275.98456571
< 0.1%
272.39038471
< 0.1%
271.11810471
< 0.1%
263.57915561
< 0.1%
259.22022991
< 0.1%
257.09367281
< 0.1%
253.58346871
< 0.1%
252.21036811
< 0.1%
251.2993121
< 0.1%
245.89649671
< 0.1%

Days Since Last Purchase
Real number (ℝ≥0)

Distinct181
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.105
Minimum0
Maximum180
Zeros36
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2023-03-16T12:09:48.457706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q145
median89
Q3134
95-th percentile170
Maximum180
Range180
Interquartile range (IQR)89

Descriptive statistics

Standard deviation51.67243789
Coefficient of variation (CV)0.5799050322
Kurtosis-1.182504745
Mean89.105
Median Absolute Deviation (MAD)45
Skewness0.01195596315
Sum623735
Variance2670.040838
MonotonicityNot monotonic
2023-03-16T12:09:48.577595image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9161
 
0.9%
9253
 
0.8%
7352
 
0.7%
14452
 
0.7%
8151
 
0.7%
10251
 
0.7%
3750
 
0.7%
1750
 
0.7%
4550
 
0.7%
1350
 
0.7%
Other values (171)6480
92.6%
ValueCountFrequency (%)
036
0.5%
140
0.6%
240
0.6%
336
0.5%
433
0.5%
546
0.7%
640
0.6%
730
0.4%
838
0.5%
938
0.5%
ValueCountFrequency (%)
18028
0.4%
17933
0.5%
17826
0.4%
17741
0.6%
17635
0.5%
17527
0.4%
17436
0.5%
17325
0.4%
17236
0.5%
17143
0.6%

Number of Returns
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.519285714
Minimum0
Maximum5
Zeros1132
Zeros (%)16.2%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2023-03-16T12:09:48.658210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.701272245
Coefficient of variation (CV)0.675299445
Kurtosis-1.264063584
Mean2.519285714
Median Absolute Deviation (MAD)1
Skewness-0.01710523741
Sum17635
Variance2.894327251
MonotonicityNot monotonic
2023-03-16T12:09:48.842834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
41203
17.2%
31177
16.8%
11170
16.7%
51162
16.6%
21156
16.5%
01132
16.2%
ValueCountFrequency (%)
01132
16.2%
11170
16.7%
21156
16.5%
31177
16.8%
41203
17.2%
51162
16.6%
ValueCountFrequency (%)
51162
16.6%
41203
17.2%
31177
16.8%
21156
16.5%
11170
16.7%
01132
16.2%

Total Values of Returns
Real number (ℝ)

UNIQUE

Distinct7000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.79926437
Minimum-55.8173199
Maximum160.3949982
Zeros0
Zeros (%)0.0%
Negative357
Negative (%)5.1%
Memory size54.8 KiB
2023-03-16T12:09:48.927455image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-55.8173199
5-th percentile-0.2761370843
Q129.39278708
median50.08932643
Q369.70943178
95-th percentile100.3043249
Maximum160.3949982
Range216.2123181
Interquartile range (IQR)40.3166447

Descriptive statistics

Standard deviation30.28368044
Coefficient of variation (CV)0.6081150158
Kurtosis-0.06219464274
Mean49.79926437
Median Absolute Deviation (MAD)20.14057542
Skewness0.007173978838
Sum348594.8506
Variance917.101301
MonotonicityNot monotonic
2023-03-16T12:09:49.043314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.110946531
 
< 0.1%
26.68932691
 
< 0.1%
129.75675151
 
< 0.1%
23.195419321
 
< 0.1%
10.88505211
 
< 0.1%
45.028087581
 
< 0.1%
60.330129381
 
< 0.1%
78.027928021
 
< 0.1%
-13.510304451
 
< 0.1%
26.108353511
 
< 0.1%
Other values (6990)6990
99.9%
ValueCountFrequency (%)
-55.81731991
< 0.1%
-55.802080861
< 0.1%
-54.551041521
< 0.1%
-52.417609251
< 0.1%
-43.982461711
< 0.1%
-42.212966451
< 0.1%
-41.746112721
< 0.1%
-39.628312261
< 0.1%
-37.189057531
< 0.1%
-36.170049291
< 0.1%
ValueCountFrequency (%)
160.39499821
< 0.1%
153.44001861
< 0.1%
150.98142041
< 0.1%
149.86177071
< 0.1%
145.21987181
< 0.1%
144.80311351
< 0.1%
141.67129081
< 0.1%
140.44967971
< 0.1%
139.17244271
< 0.1%
139.05022791
< 0.1%

Locations
Categorical

UNIFORM

Distinct20
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size54.8 KiB
Bangalore
 
350
Delhi
 
350
Nagpur
 
350
Visakhapatnam
 
350
Lucknow
 
350
Other values (15)
5250 

Length

Max length13
Median length8
Mean length6.8
Min length4

Characters and Unicode

Total characters47600
Distinct characters33
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBangalore
2nd rowDelhi
3rd rowJaipur
4th rowNashik
5th rowBangalore

Common Values

ValueCountFrequency (%)
Bangalore350
 
5.0%
Delhi350
 
5.0%
Nagpur350
 
5.0%
Visakhapatnam350
 
5.0%
Lucknow350
 
5.0%
Kanpur350
 
5.0%
Vadodara350
 
5.0%
Kolkata350
 
5.0%
Agra350
 
5.0%
Patna350
 
5.0%
Other values (10)3500
50.0%

Length

2023-03-16T12:09:49.143324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bangalore350
 
5.0%
delhi350
 
5.0%
jaipur350
 
5.0%
nashik350
 
5.0%
ludhiana350
 
5.0%
pune350
 
5.0%
ahmedabad350
 
5.0%
bhopal350
 
5.0%
surat350
 
5.0%
mumbai350
 
5.0%
Other values (10)3500
50.0%

Most occurring characters

ValueCountFrequency (%)
a9800
20.6%
n3150
 
6.6%
r2800
 
5.9%
u2800
 
5.9%
h2450
 
5.1%
d2450
 
5.1%
i2450
 
5.1%
e2100
 
4.4%
p1750
 
3.7%
o1750
 
3.7%
Other values (23)16100
33.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter40600
85.3%
Uppercase Letter7000
 
14.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a9800
24.1%
n3150
 
7.8%
r2800
 
6.9%
u2800
 
6.9%
h2450
 
6.0%
d2450
 
6.0%
i2450
 
6.0%
e2100
 
5.2%
p1750
 
4.3%
o1750
 
4.3%
Other values (10)9100
22.4%
Uppercase Letter
ValueCountFrequency (%)
P700
10.0%
K700
10.0%
A700
10.0%
B700
10.0%
L700
10.0%
V700
10.0%
N700
10.0%
H350
 
5.0%
D350
 
5.0%
M350
 
5.0%
Other values (3)1050
15.0%

Most occurring scripts

ValueCountFrequency (%)
Latin47600
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a9800
20.6%
n3150
 
6.6%
r2800
 
5.9%
u2800
 
5.9%
h2450
 
5.1%
d2450
 
5.1%
i2450
 
5.1%
e2100
 
4.4%
p1750
 
3.7%
o1750
 
3.7%
Other values (23)16100
33.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII47600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a9800
20.6%
n3150
 
6.6%
r2800
 
5.9%
u2800
 
5.9%
h2450
 
5.1%
d2450
 
5.1%
i2450
 
5.1%
e2100
 
4.4%
p1750
 
3.7%
o1750
 
3.7%
Other values (23)16100
33.8%

Interactions

2023-03-16T12:09:42.637132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:25.941106image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:27.185172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:28.616630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:29.832157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:31.393757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:32.826016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:34.390315image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:35.669228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:37.189378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:38.510174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:39.863666image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:41.201059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:42.725782image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:26.042715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:27.280953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:28.687964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:29.926088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:31.490122image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:32.937647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:34.480966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:35.767612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:37.279617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:38.601352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:39.955041image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:41.295943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:42.826255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:26.156096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:27.398725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:28.802456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:30.034364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:31.603253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:33.047540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:34.569537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:35.904009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:37.384694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:38.701829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:40.060954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:41.406468image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:42.923375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:26.245048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:27.481990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:28.891385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:30.130994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:31.708840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:33.160731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:34.678764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:36.009536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:37.480065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:38.796317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:40.157413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:41.503940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:43.043851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:26.345804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:27.600565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:28.989132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:30.235775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:31.814568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:33.282264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:34.778469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:36.121706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:37.582237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:38.899225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:40.266736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:41.603633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:43.163996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:26.437860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:27.701197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:29.077693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:30.340447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:31.930606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:33.385002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:34.874524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:36.209196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:37.703907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:38.994265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:40.350539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:41.700932image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:43.261238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:26.530335image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:27.801262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:29.168731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:30.438711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:32.037404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:33.486460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:34.971926image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:36.316372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:37.828615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:39.088393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:40.465772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:41.799228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:43.345232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:26.622024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:27.898543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:29.261202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:30.535951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:32.143981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:33.592861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:35.067711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:36.434504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:37.924322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:39.181904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:40.560150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:41.889452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:43.465127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:26.725961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:27.989349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:29.361416image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:30.660991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:32.261461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:33.712651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:35.177815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:36.546155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:38.031353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:39.286114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:40.666471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:42.148924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:43.562728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:26.818658image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:28.106316image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:29.456416image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:30.791650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:32.375683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:33.837171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:35.277404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:36.652208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:38.126620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:39.475550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:40.767568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:42.245900image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:43.661054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:26.911067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:28.207385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:29.551026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:30.900942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:32.488791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:33.941188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:35.378569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:36.869613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:38.223044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:39.579671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:40.866523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:42.330427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:43.767093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:27.005915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:28.426286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:29.647361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:31.175058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:32.606919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:34.182303image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:35.480542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:36.983046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:38.323874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:39.677671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:40.975768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:42.456242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:43.844462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:27.085103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:28.522380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:29.723256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:31.292444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:32.719034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:34.298212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:35.574976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:37.092123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:38.411135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:39.770042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:41.089514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-16T12:09:42.548296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-03-16T12:09:49.234770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2023-03-16T12:09:49.428121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-03-16T12:09:49.622261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-03-16T12:09:49.796494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-03-16T12:09:49.976349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2023-03-16T12:09:50.107823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-03-16T12:09:44.019971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-16T12:09:44.388294image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Customer IDGenderAgeIncomeOccupationMarital StatusNo. Family MembersHome OwnershipHome ValueYears in Current HomeCredit ScoreNumber of Credit CardsTotal Credit Card LimitTotal Credit Card BalanceProduct IDProducts PurchasedNumber of Online PurchasesAverage Purchase ValueLast Purchase ValueDays Since Last PurchaseNumber of ReturnsTotal Values of ReturnsLocations
0C001Female2327275StudentSingle2Own4427971394051233122979P001Clothing8142.77695350.13132529120.110947Bangalore
1C002Female1835579StudentSingle3Own1820901793211573318890P002Jewelry16152.68842340.42363166433.868237Delhi
2C003Male4559347HomemakerSingle2Own9736841255252276914721P003Jewelry3115.26167488.014050131494.178999Jaipur
3C004Female1927105StudentMarried3Own9466975575912721491P004Books148.428081192.53289150551.754754Nashik
4C005Female4547566HomemakerSingle4Own9384937858582782842P005Jewelry13183.458441233.079310171376.830901Bangalore
5C006Male2648031StudentDivorced5Rent6648141980022456619665P006Books14158.440858104.0209631653107.127717Ludhiana
6C007Female2037707TechnicianWidowed2Own7172121862811707911196P007Books1272.058265131.94753191348.380474Bangalore
7C008Male6135280Self-employedSingle4Rent4592781082952008814159P008Clothing19165.900343163.1241161442107.574943Pune
8C009Male2659207HomemakerMarried2Own56088355805105355979P009Clothing382.948927155.8063861024103.462284Ahmedabad
9C010Male2442999HomemakerSingle3Rent88944159441136422508P010Electronics1423.331704208.430396105552.655495Jaipur

Last rows

Customer IDGenderAgeIncomeOccupationMarital StatusNo. Family MembersHome OwnershipHome ValueYears in Current HomeCredit ScoreNumber of Credit CardsTotal Credit Card LimitTotal Credit Card BalanceProduct IDProducts PurchasedNumber of Online PurchasesAverage Purchase ValueLast Purchase ValueDays Since Last PurchaseNumber of ReturnsTotal Values of ReturnsLocations
6990C6991Male2453655ExecutiveDivorced4Rent31341796185862614108P6991Home Appliances771.701238179.7154821431125.193790Lucknow
6991C6992Female5140004ManagerWidowed3Own35277288745126951441P6992Home Appliances1663.66835475.09254456318.132531Ahmedabad
6992C6993Female5437405StudentMarried2Own38535715722759212387P6993Jewelry2130.711668174.7728191702118.620695Kolkata
6993C6994Female2527759StudentWidowed5Own701752665652170317331P6994Jewelry14122.38013482.5542517812.795735Kolkata
6994C6995Female5523890StudentMarried4Rent322742592732780115866P6995Jewelry336.398758118.028381632-4.169920Patna
6995C6996Male6252159ProfessionalSingle5Own449503452311341616224P6996Beauty Products15101.121872124.81475621056.564169Bangalore
6996C6997Male5627406Self-employedSingle2Own804846189681196618915P6997Outdoor Gear9165.191947153.2267933487.086811Pune
6997C6998Female1841671Self-employedWidowed4Own35672012617587695717P6998Home Appliances2046.54446285.1879436037.188616Mumbai
6998C6999Male5221359ExecutiveDivorced3Own978818176185123957641P6999Electronics298.275850151.66705372346.998330Bangalore
6999C7000Female3244999TechnicianWidowed5Own166775659122011720328P7000Home Appliances14118.425279160.690982110155.199802Bangalore